U.S. patent number 10,546,123 [Application Number 15/632,280] was granted by the patent office on 2020-01-28 for systems and methods for identifying malicious computer files.
This patent grant is currently assigned to CA, Inc.. The grantee listed for this patent is Symantec Corporation. Invention is credited to Mark Kennedy, Barry Laffoon, Qichao Lan, XueFeng Tian.
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United States Patent |
10,546,123 |
Lan , et al. |
January 28, 2020 |
Systems and methods for identifying malicious computer files
Abstract
A computer-implemented method for identifying malicious computer
files may include (i) receiving, by a computing device, a set of
files from a set of client devices, (ii) performing, by the
computing device, a machine learning classification of file
attributes on the set of files, (iii) determining, based on the
machine learning classification, a node pattern of a suspicious
file in the set of files, (iv) calculating, by hashing the node
pattern, a file prevalence score of the suspicious file, and (v)
performing, by the computing device, a security action based on the
file prevalence score of the suspicious file. Various other
methods, systems, and computer-readable media are also
disclosed.
Inventors: |
Lan; Qichao (Culver City,
CA), Kennedy; Mark (Gardena, CA), Tian; XueFeng
(Culver City, CA), Laffoon; Barry (Glendale, CA) |
Applicant: |
Name |
City |
State |
Country |
Type |
Symantec Corporation |
Mountain View |
CA |
US |
|
|
Assignee: |
CA, Inc. (San Jose,
CA)
|
Family
ID: |
69180084 |
Appl.
No.: |
15/632,280 |
Filed: |
June 23, 2017 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06F
21/56 (20130101); G06F 21/568 (20130101); G06N
5/003 (20130101); G06N 20/00 (20190101); G06N
20/20 (20190101); G06N 20/10 (20190101); G06N
3/08 (20130101) |
Current International
Class: |
G06F
21/56 (20130101); G06N 20/00 (20190101) |
Field of
Search: |
;726/22-25 ;706/12,20,21
;707/737,747,797,758 ;713/165,177 |
References Cited
[Referenced By]
U.S. Patent Documents
Primary Examiner: Najjar; Saleh
Assistant Examiner: Almaghayreh; Khalid M
Attorney, Agent or Firm: FisherBroyles, LLP
Claims
What is claimed is:
1. A computer-implemented method for identifying malicious computer
files, at least a portion of the method being performed by a
computing device comprising at least one processor, the method
comprising: receiving, by the computing device, a set of files from
a set of client devices; performing, by the computing device, a
machine learning classification of file attributes on the set of
files; determining, based on the machine learning classification, a
node pattern of a suspicious file in the set of files, wherein each
node of the node pattern comprises a condition for classification;
calculating a file prevalence score of the suspicious file by:
hashing the node pattern by aggregating a set of nodes into a
single hash value; identifying a family of files for the suspicious
file based on the hash value, wherein the family of files comprises
files with the same hash value; and calculating a prevalence of the
hash value in the set of files, wherein the prevalence of the hash
value indicates the prevalence of the family of files; and
performing, by the computing device, a security action based on the
file prevalence score of the suspicious file.
2. The method of claim 1, wherein the file attributes comprise
static attributes derived from at least one of: a header of a file;
metadata of the file; and a scan of file contents.
3. The method of claim 1, wherein performing the machine learning
classification comprises: extracting a set of features from the
file attributes; and constructing a set of decision trees to
classify the set of features.
4. The method of claim 3, wherein determining the node pattern of
the suspicious file comprises: classifying the suspicious file
using at least one decision tree in the set of decision trees; and
identifying a set of nodes based on the decision tree
classification.
5. The method of claim 1, wherein performing the security action
comprises at least one of: flagging the suspicious file as
potentially malicious; flagging the family of files as potentially
malicious; quarantining the suspicious file on a client device;
quarantining another file in the family of files on the client
device; alerting an administrator about the suspicious file; and
alerting an administrator about the family of files.
6. A system for identifying malicious computer files, the system
comprising: a reception module, stored in memory, that receives, by
a computing device, a set of files from a set of client devices; a
performance module, stored in memory, that performs, by the
computing device, a machine learning classification of file
attributes on the set of files; a determination module, stored in
memory, that determines, based on the machine learning
classification, a node pattern of a suspicious file in the set of
files, wherein each node of the node pattern comprises a condition
for classification; a calculation module, stored in memory, that
calculates a file prevalence score of the suspicious file by:
hashing the node pattern by aggregating a set of nodes into a
single hash value; identifying a family of files for the suspicious
file based on the hash value, wherein the family of files comprises
files with the same hash value; and calculating a prevalence of the
hash value in the set of files, wherein the prevalence of the hash
value indicates the prevalence of the family of files; a security
module, stored in memory, that performs, by the computing device, a
security action based on the file prevalence score of the
suspicious file; and at least one processor that executes the
reception module, the performance module, the determination module,
the calculation module, and the security module.
7. The system of claim 6, wherein the file attributes comprise
static attributes derived from at least one of: a header of a file;
metadata of the file; and a scan of file contents.
8. The system of claim 6, wherein the performance module performs
the machine learning classification by: extracting a set of
features from the file attributes; and constructing a set of
decision trees to classify the set of features.
9. The system of claim 8, wherein the determination module
determines the node pattern of the suspicious file by: classifying
the suspicious file using at least one decision tree in the set of
decision trees; and identifying a set of nodes based on the
decision tree classification.
10. The system of claim 6, wherein the security module performs the
security action by at least one of: flagging the suspicious file as
potentially malicious; flagging the family of files as potentially
malicious; quarantining the suspicious file on a client device;
quarantining another file in the family of files on the client
device; alerting an administrator about the suspicious file; and
alerting an administrator about the family of files.
11. A non-transitory computer-readable medium comprising one or
more computer-executable instructions that, when executed by at
least one processor of a computing device, cause the computing
device to: receive, by the computing device, a set of files from a
set of client devices; perform, by the computing device, a machine
learning classification of file attributes on the set of files;
determine, based on the machine learning classification, a node
pattern of a suspicious file in the set of files, wherein each node
of the node pattern comprises a condition for classification;
calculate a file prevalence score of the suspicious file by:
hashing the node pattern by aggregating a set of nodes into a
single hash value; identifying a family of files for the suspicious
file based on the hash value, wherein the family of files comprises
files with the same hash value; and calculating a prevalence of the
hash value in the set of files, wherein the prevalence of the hash
value indicates the prevalence of the family of files; and perform,
by the computing device, a security action based on the file
prevalence score of the suspicious file.
12. The non-transitory computer-readable medium of claim 11,
wherein the file attributes comprise static attributes derived from
at least one of: a header of a file; metadata of the file; and a
scan of file contents.
13. The non-transitory computer-readable medium of claim 11,
wherein the computer-executable instructions cause the computing
device to perform the machine learning classification by:
extracting a set of features from the file attributes; and
constructing a set of decision trees to classify the set of
features.
14. The non-transitory computer-readable medium of claim 13,
wherein the computer-executable instructions cause the computing
device to determine the node pattern of the suspicious file by:
classifying the suspicious file using at least one decision tree in
the set of decision trees; and identifying a set of nodes based on
the decision tree classification.
Description
BACKGROUND
Computer security software may often use various file attributes to
help determine whether certain files are potentially malicious or
contain malware. To detect potentially malicious files, some
security software may also use file prevalence to determine whether
a computer file is unique or unusual. For example, a file that has
a low prevalence within a large number and variety of known files
may be a file that is highly unusual and, therefore, suspicious. On
the contrary, a file with a high prevalence score may suggest that
the file is common and likely to be legitimate, since many
computers or users have the same or a similar file. File prevalence
may also be used to determine the scope of potential threats caused
by the proliferation of a malicious file.
Traditionally, a hash function may be performed on computer files
to help identify the same files on multiple computing systems and
to preserve file integrity. File prevalence may then be calculated
on file hash values across multiple systems to determine whether
files are common to similar systems. However, in some cases, minor
changes to a file or a hash algorithm may result in a vastly
different hash value for a file. The resulting hash value may
appear to be uncommon and indicate a low file prevalence score, but
the file may actually be very common. For example, a malicious file
may make slight changes to escape detection by security software if
the software only recognizes certain hash values as malware. Thus,
reliance on file hashes may cause false positives in identifying
potentially malicious files or may miss actual malicious files due
to small differences in the files. The instant disclosure,
therefore, identifies and addresses a need for improved systems and
methods for identifying malicious computer files.
SUMMARY
As will be described in greater detail below, the instant
disclosure describes various systems and methods for identifying
malicious computer files. In one example, a computer-implemented
method for identifying malicious computer files may include (i)
receiving, by a computing device, a set of files from a set of
client devices, (ii) performing, by the computing device, a machine
learning classification of file attributes on the set of files,
(iii) determining, based on the machine learning classification, a
node pattern of a suspicious file in the set of files, (iv)
calculating, by hashing the node pattern, a file prevalence score
of the suspicious file, and (v) performing, by the computing
device, a security action based on the file prevalence score of the
suspicious file.
In one embodiment, the file attributes may include static
attributes derived from a header of a file, metadata of the file,
and/or a scan of file contents. In this embodiment, performing the
machine learning classification may include extracting a set of
features from the file attributes and constructing a set of
decision trees to classify the set of features.
In some examples, determining the node pattern of the suspicious
file may include classifying the suspicious file using one or more
decision trees in the set of decision trees and identifying a set
of nodes based on the decision tree classification. In these
examples, hashing the node pattern may include aggregating the set
of nodes into a single hash value. In additional examples,
calculating the file prevalence score may include calculating a
prevalence of the hash value in the set of files and/or identifying
a family of files for the suspicious file based on the hash
value.
In some embodiments, performing the security action may include
flagging the suspicious file as potentially malicious, quarantining
the suspicious file on a client device, and/or alerting an
administrator about the suspicious file. Additionally or
alternatively, performing the security action may include flagging
the family of files as potentially malicious, quarantining another
file in the family of files on the client device, and/or alerting
an administrator about the family of files.
In one embodiment, a system for implementing the above-described
method may include (i) a reception module, stored in memory, that
receives, by a computing device, a set of files from a set of
client devices, (ii) a performance module, stored in memory, that
performs, by the computing device, a machine learning
classification of file attributes on the set of files, (iii) a
determination module, stored in memory, that determines, based on
the machine learning classification, a node pattern of a suspicious
file in the set of files, (iv) a calculation module, stored in
memory, that calculates, by hashing the node pattern, a file
prevalence score of the suspicious file, and (v) a security module,
stored in memory, that performs, by the computing device, a
security action based on the file prevalence score of the
suspicious file. In addition, the system may include at least one
processor that executes the reception module, the performance
module, the determination module, the calculation module, and the
security module.
In some examples, the above-described method may be encoded as
computer-readable instructions on a non-transitory
computer-readable medium. For example, a computer-readable medium
may include one or more computer-executable instructions that, when
executed by at least one processor of a computing device, may cause
the computing device to (i) receive a set of files from a set of
client devices, (ii) perform a machine learning classification of
file attributes on the set of files, (iii) determine, based on the
machine learning classification, a node pattern of a suspicious
file in the set of files, (iv) calculate, by hashing the node
pattern, a file prevalence score of the suspicious file, and (v)
perform a security action based on the file prevalence score of the
suspicious file.
Features from any of the above-mentioned embodiments may be used in
combination with one another in accordance with the general
principles described herein. These and other embodiments, features,
and advantages will be more fully understood upon reading the
following detailed description in conjunction with the accompanying
drawings and claims.
BRIEF DESCRIPTION OF THE DRAWINGS
The accompanying drawings illustrate a number of example
embodiments and are a part of the specification. Together with the
following description, these drawings demonstrate and explain
various principles of the instant disclosure.
FIG. 1 is a block diagram of an example system for identifying
malicious computer files.
FIG. 2 is a block diagram of an additional example system for
identifying malicious computer files.
FIG. 3 is a flow diagram of an example method for identifying
malicious computer files.
FIG. 4 is a block diagram of an example creation of an example set
of decision trees.
FIG. 5 is a block diagram of an example hash of an example set of
nodes for an example suspicious file.
FIG. 6 is a block diagram of an example family of files for an
example suspicious file.
FIG. 7 is a block diagram of an example computing system capable of
implementing one or more of the embodiments described and/or
illustrated herein.
FIG. 8 is a block diagram of an example computing network capable
of implementing one or more of the embodiments described and/or
illustrated herein.
Throughout the drawings, identical reference characters and
descriptions indicate similar, but not necessarily identical,
elements. While the example embodiments described herein are
susceptible to various modifications and alternative forms,
specific embodiments have been shown by way of example in the
drawings and will be described in detail herein. However, the
example embodiments described herein are not intended to be limited
to the particular forms disclosed. Rather, the instant disclosure
covers all modifications, equivalents, and alternatives falling
within the scope of the appended claims.
DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS
The present disclosure is generally directed to systems and methods
for identifying malicious computer files. As will be explained in
greater detail below, by classifying files with decision trees, the
systems and methods described herein may use classification node
patterns to determine file prevalence for files in a network of
computing devices. For example, by hashing a node pattern for a
file, the disclosed systems and methods may use the hash value to
determine the file prevalence based on various file attributes. In
an organization or a known network of computing devices, the
systems and methods described herein may use the file prevalence to
determine the prevalence and scope of threats caused by malicious
files that may infect the network. Furthermore, by using hashes of
node patterns rather than individual files, the disclosed systems
and methods may identify entire families of files that may be
potentially malicious.
In addition, the systems and methods described herein may improve
the functioning of a computing device by performing security
actions to mitigate potentially malicious files. These systems and
methods may also improve the fields of cybersecurity and/or network
security by identifying and preventing attacks caused by malicious
files. Thus, the disclosed systems and methods may improve the
prevention of potential malware threats with improved malicious
file detection.
The following will provide, with reference to FIGS. 1 and 2,
detailed descriptions of example systems for identifying malicious
computer files. Detailed descriptions of corresponding
computer-implemented methods will also be provided in connection
with FIG. 3. In addition, detailed descriptions of an example
creation of an example set of decision trees will be provided in
connection with FIG. 4. Detailed descriptions of an example hash of
an example set of nodes for an example suspicious file will also be
provided in connection with FIG. 5. Furthermore, detailed
descriptions of an example family of files for an example
suspicious file will be provided in connection with FIG. 6.
Finally, detailed descriptions of an example computing system and
network architecture capable of implementing one or more of the
embodiments described herein will be provided in connection with
FIGS. 7 and 8, respectively.
FIG. 1 is a block diagram of example system 100 for identifying
malicious computer files. As illustrated in this figure, example
system 100 may include one or more modules 102 for performing one
or more tasks. For example, and as will be explained in greater
detail below, modules 102 may include a reception module 104 that
receives, by a computing device, a set of files from a set of
client devices. Modules 102 may additionally include a performance
module 106 that performs, by the computing device, a machine
learning classification of file attributes on the set of files. As
used herein, the term "machine learning" generally refers to a
computational algorithm that may learn from data in order to make
predictions. Examples of machine learning may include, without
limitation, support vector machines, neural networks, clustering,
decision trees, regression analysis, classification, variations or
combinations of one or more of the same, and/or any other suitable
supervised, semi-supervised, or unsupervised methods. Notably, a
machine learning classification may allow a classifier to classify
data into different categories based on various attributes of the
data.
As illustrated in FIG. 1, modules 102 may also include a
determination module 108 that determines, based on the machine
learning classification, a node pattern of a suspicious file in the
set of files. The term "node," as used herein, generally refers to
a data value or condition used as a part of a tree data structure,
which may be used as a classifier. Thus, the term "node pattern,"
as used herein, generally refers to a set of nodes or
classifications derived from multiple classifiers.
Modules 102 may additionally include a calculation module 110 that
calculates, by hashing the node pattern, a file prevalence score of
the suspicious file. The term "hash," as used herein, generally
refers to a process of converting digital data to a fixed value.
The term "file prevalence," as used herein, generally refers to an
indication of how common a file is among a set of files.
Modules 102 may further include a security module 112 that
performs, by the computing device, a security action based on the
file prevalence score of the suspicious file. Although illustrated
as separate elements, one or more of modules 102 in FIG. 1 may
represent portions of a single module or application or multiple
modules or applications.
In certain embodiments, one or more of modules 102 in FIG. 1 may
represent one or more software applications or programs that, when
executed by a computing device, may cause the computing device to
perform one or more tasks. For example, and as will be described in
greater detail below, one or more of modules 102 may represent
modules stored and configured to run on one or more computing
devices, such as the devices illustrated in FIG. 2 (e.g., computing
device 202 and/or set of client devices 206). One or more of
modules 102 in FIG. 1 may also represent all or portions of one or
more special-purpose computers configured to perform one or more
tasks.
As illustrated in FIG. 1, example system 100 may also include one
or more memory devices, such as memory 140. Memory 140 generally
represents any type or form of volatile or non-volatile storage
device or medium capable of storing data and/or computer-readable
instructions. In one example, memory 140 may store, load, and/or
maintain one or more of modules 102. Examples of memory 140
include, without limitation, Random Access Memory (RAM), Read Only
Memory (ROM), flash memory, Hard Disk Drives (HDDs), Solid-State
Drives (SSDs), optical disk drives, caches, variations or
combinations of one or more of the same, and/or any other suitable
storage memory.
As illustrated in FIG. 1, example system 100 may also include one
or more physical processors, such as physical processor 130.
Physical processor 130 generally represents any type or form of
hardware-implemented processing unit capable of interpreting and/or
executing computer-readable instructions. In one example, physical
processor 130 may access and/or modify one or more of modules 102
stored in memory 140. Additionally or alternatively, physical
processor 130 may execute one or more of modules 102 to facilitate
identifying malicious computer files. Examples of physical
processor 130 include, without limitation, microprocessors,
microcontrollers, Central Processing Units (CPUs),
Field-Programmable Gate Arrays (FPGAs) that implement softcore
processors, Application-Specific Integrated Circuits (ASICs),
portions of one or more of the same, variations or combinations of
one or more of the same, and/or any other suitable physical
processor.
As illustrated in FIG. 1, example system 100 may also include one
or more databases, such as database 120. In one example, database
120 may be configured to store a node pattern 122, which may
include a set of node classification for a file, and/or a file
prevalence score 124, which may include a commonness for the file
among a set of files. Database 120 may represent portions of a
single database or computing device or a plurality of databases or
computing devices. For example, database 120 may represent a
portion of computing device 202 and/or set of client devices 206 in
FIG. 2. Alternatively, database 120 in FIG. 1 may represent one or
more physically separate devices capable of being accessed by a
computing device, such as computing device 202 and/or set of client
devices 206 in FIG. 2.
Example system 100 in FIG. 1 may be implemented in a variety of
ways. For example, all or a portion of example system 100 may
represent portions of example system 200 in FIG. 2. As shown in
FIG. 2, system 200 may include a computing device 202 in
communication with a set of client devices 206 via a network 204.
In one example, all or a portion of the functionality of modules
102 may be performed by computing device 202, a device in set of
client devices 206, and/or any other suitable computing system.
Similarly, devices in set of client devices 206 and/or computing
device 202 may be merged into a single machine or computing system
such that the functionality of each of modules 102 is provided
within a single device.
As will be described in greater detail below, one or more of
modules 102 from FIG. 1 may, when executed by at least one
processor of computing device 202 and/or set of client devices 206,
enable computing device 202 and/or set of client devices 206 to
calculate file prevalence for a set of files. For example, and as
will be described in greater detail below, reception module 104 may
receive a set of files 208 from set of client devices 206.
Performance module 106 may perform a machine learning
classification 212 of file attributes on set of files 208.
Determination module 108 may determine, based on classification
212, node pattern 122 of a suspicious file 210 in set of files 208.
Calculation module 110 may calculate, by hashing node pattern 122,
file prevalence score 124 of suspicious file 210. Security module
112 may perform a security action 214 based on file prevalence
score 124 of suspicious file 210.
In the example of FIG. 2, and as will be explained in greater
detail below, computing device 202 may first receive set of files
208 from set of client devices 206 via network 204. Computing
device 202 may then classify set of files 208 using classification
212. Next, computing device 202 may identify suspicious file 210
and determine node pattern 122 for suspicious file 210 based on
classification 212. Computing device 202 may also calculate file
prevalence score 124 for suspicious file 210 through hashing node
pattern 122. Finally, computing device 202 may perform security
action 214 on set of client devices 206.
Computing device 202 and/or set of client devices 206 generally
represent any type or form of computing device capable of reading
computer-executable instructions. For example, computing device 202
may represent an administrative device that monitors set of client
devices 206, and devices in set of client devices 206 may represent
endpoint devices running client-side security software. Additional
examples of computing device 202 and/or set of client devices 206
include, without limitation, laptops, tablets, desktops, servers,
cellular phones, Personal Digital Assistants (PDAs), multimedia
players, embedded systems, wearable devices (e.g., smart watches,
smart glasses, etc.), gaming consoles, combinations of one or more
of the same, and/or any other suitable computing device.
Network 204 generally represents any medium or architecture capable
of facilitating communication or data transfer. In one example,
network 204 may facilitate communication between computing device
202 and set of client devices 206. In this example, network 204 may
facilitate communication or data transfer using wireless and/or
wired connections. Examples of network 204 include, without
limitation, an intranet, a Wide Area Network (WAN), a Local Area
Network (LAN), a Personal Area Network (PAN), the Internet, Power
Line Communications (PLC), a cellular network (e.g., a Global
System for Mobile Communications (GSM) network), portions of one or
more of the same, variations or combinations of one or more of the
same, and/or any other suitable network.
FIG. 3 is a flow diagram of an example computer-implemented method
300 for identifying malicious computer files. The steps shown in
FIG. 3 may be performed by any suitable computer-executable code
and/or computing system, including system 100 in FIG. 1, system 200
in FIG. 2, and/or variations or combinations of one or more of the
same. In one example, each of the steps shown in FIG. 3 may
represent an algorithm whose structure includes and/or is
represented by multiple sub-steps, examples of which will be
provided in greater detail below.
As illustrated in FIG. 3, at step 302, one or more of the systems
described herein may receive, by a computing device, a set of files
from a set of client devices. For example, reception module 104
may, as part of computing device 202 in FIG. 2, receive set of
files 208 from set of client devices 206.
Reception module 104 may receive set of files 208 in a variety of
ways. In one example, reception module 104 may monitor all
computing devices connected to network 204, such as set of client
devices 206, and periodically request files from the connected
devices. In another example, reception module 104 may collect files
from newly connected computing devices and/or collect new files
from existing computing devices to update set of files 208.
Furthermore, in some examples, client-side security software on set
of client devices 206 may send set of files 208 and/or information
about set of files 208 to computing device 202 via network 204 for
security analysis.
Returning to FIG. 3, at step 304, one or more of the systems
described herein may perform, by the computing device, a machine
learning classification of file attributes on the set of files. For
example, performance module 106 may, as part of computing device
202 in FIG. 2, perform machine learning classification 212 of file
attributes on set of files 208.
Performance module 106 may perform classification 212 in a variety
of ways. In one embodiment, the file attributes may include static
attributes derived from a header of a file, metadata of the file,
and/or a scan of file contents. The term "header," as used herein,
generally refers to descriptive information about a file. A file
header may contain information about a file format, a file size, an
identifier, and/or other specific information that describes a
file. The term "metadata," as used herein, generally refers to data
that describes a file and/or the structure of a file. Some file
metadata may or may not be included in a file header. Additionally,
the scan of file contents may include details about the file that
may be found in a file header or file metadata and/or may include
additional details about the contents of the file. The file
attributes may include only attributes that are derived from set of
files 208 and/or information about set of files 208.
In some examples, performance module 106 may perform classification
212 by extracting a set of features from the file attributes and
constructing a set of decision trees to classify the set of
features. The term "feature," as used herein, generally refers to a
value or vector derived from data that allows it to be measured
and/or interpreted as part of a machine learning method. Examples
of features may include numerical data that quantizes a factor,
textual data used in pattern recognition, graphical data, or any
other format of data that may be analyzed using statistical methods
or machine learning. The term "decision tree," as used herein,
generally refers to a predictive model with a branching structure
that graphs outcomes of observations. Decision trees may be created
using a set of initial files or data to train the trees to
accurately classify files using the features derived from the file
attributes. The set of decision trees may also be trained to
specifically use features that may indicate malicious files.
As illustrated in FIG. 4, set of files 208 may include file
attributes 402, which may then be converted into a set of features
404. Performance module 106 may create decision trees 406(1),
406(2), and 406(3) using set of features 404. In this example, set
of features 404 may be adequately classified with three decision
trees. In alternate examples, performance module 106 may construct
fewer or more decision trees for the set of decision trees. The
number of decision trees in the set of decision trees may be
adjusted based on a desired accuracy or granularity of
classification. For example, the set of decision trees may classify
all files similar to a known malicious file in the same
classification such that performance module 106 may determine files
with similar attributes are related malicious files.
Returning to FIG. 3, at step 306, one or more of the systems
described herein may determine, based on the machine learning
classification, a node pattern of a suspicious file in the set of
files. For example, determination module 108 may, as part of
computing device 202 in FIG. 2, determine, based on classification
212, node pattern 122 of suspicious file 210 in set of files
208.
Determination module 108 may determine node pattern 122 in a
variety of ways. In some embodiments, determination module 108 may
determine node pattern 122 of suspicious file 210 by classifying
suspicious file 210 using one or more decision trees in the set of
decision trees and identifying a set of nodes based on the decision
tree classification. In these embodiments, each node in the set of
nodes may be a specific classification result and/or other value
derived from a decision tree. Files that are the same or highly
similar may result in the same node classifications.
As shown in FIG. 5, determination module 108 may classify
suspicious file 210 using decision trees 406(1), 406(2), and 406(3)
to obtain a node 502(1), a node 502(2), and a node 502(3),
respectively. Determination module 108 may then derive node pattern
122 from nodes 502(1), 502(2), and 502(3). In alternate examples,
determination module 108 may use one or two of decision trees
406(1), 406(2), and 406(3) to classify suspicious file 210,
depending on the number of classifications needed to obtain an
accurate node pattern. For example, only decision tree 406(1) may
be required to obtain node pattern 122 that is unique compared to
node patterns for files dissimilar to suspicious file 210.
Additionally, node pattern 122 may be a common node pattern that
indicates suspicious file 210 has similar file attributes to a
number of other files.
Returning to FIG. 3, at step 308, one or more of the systems
described herein may calculate, by hashing the node pattern, a file
prevalence score of the suspicious file. For example, calculation
module 110 may, as part of computing device 202 in FIG. 2,
calculate, by hashing node pattern 122, file prevalence score 124
of suspicious file 210.
Calculation module 110 may calculate file prevalence score 124 in a
variety of ways. In one embodiment, calculation module 110 may hash
node pattern 122 by aggregating the set of nodes into a single hash
value. In this embodiment, similar node patterns may result in the
same hash value. In the example of FIG. 5, calculation module 110
may hash node pattern 122 to derive a hash value 504 (e.g.,
"XYZ789") for suspicious file 210.
In the above embodiment, calculation module 110 may then calculate
file prevalence score 124 by calculating a prevalence of hash value
504 in set of files 208 and/or identifying a family of files for
suspicious file 210 based on hash value 504. In this embodiment,
the family of files may represent files with the same hash value,
which may indicate similar node classifications from the set of
decision trees.
As illustrated in FIG. 6, suspicious file 210 may have the same
hash value (e.g., "XYZ789") as a file 602(1) and a file 602(2).
Calculation module 110 may determine that suspicious file 210, file
602(1), and file 602(2) belong to a family of files 604. A file
602(3) with a different hash value (e.g., "ABC123") may not be part
of family of files 604. In this example, files in family of files
604 with the same hash value may indicate the same or similar file
attributes that create the same node pattern for the files.
Alternatively, file 602(3) may be a unique file with attributes
dissimilar to other files in set of files 208.
Returning to FIG. 3, at step 310, one or more of the systems
described herein may perform, by the computing device, a security
action based on the file prevalence score of the suspicious file.
For example, security module 112 may, as part of computing device
202 in FIG. 2, perform security action 214 based on file prevalence
score 124 of suspicious file 210.
Security module 112 may perform security action 214 in a variety of
ways. In some examples, security module 112 may perform security
action 214 by flagging suspicious file 210 as potentially
malicious, flagging family of files 604 in FIG. 6 as potentially
malicious, quarantining suspicious file 210 on a client device,
quarantining another file in family of files 604 on the client
device, alerting an administrator about suspicious file 210, and/or
alerting an administrator about family of files 604. In these
examples, security module 112 may determine security action 214
based on file prevalence score 124 and/or family of files 604. For
example, family of files 604 may contain files known to be
malicious, and security module 112 may quarantine suspicious file
210 based on its association with family of files 604. As another
example, a large number of files in family of files 604 may
indicate a highly prevalent security threat in set of client
devices 206, and security module 112 may alert the administrator
about the prevalence of family of files 604.
In the example of FIG. 6, family of files 604 may contain malicious
files, and security module 112 may quarantine all files in family
of files 604 for all devices in set of client devices 206.
Additionally, family of files 604 may indicate a browser security
issue based on information about the files (e.g., "browser plug-in"
and "browser extension"). Security module 112 may then alert the
administrator about the potential browser security issue associated
with family of files 604.
As explained above in connection with method 300 in FIG. 3, the
disclosed systems and methods may, by performing machine learning
classification on a set of files, improve detection of the
prevalence of malicious files. Specifically, the disclosed systems
and methods may first train a set of decision trees using
attributes of the files. By classifying individual files with the
set of decision trees, the systems and methods described herein may
determine a set of nodes for each file.
By hashing the set of nodes into a single hash value, the disclosed
systems and methods may then determine the prevalence of the file
among the set of files. Additionally, the systems and methods
described herein may use the hash value to identify a family of
related or similar files. In some examples, the systems and methods
described herein may also perform a security action to mitigate
potentially malicious files that are identified through calculating
file prevalence.
As detailed above, by hashing decision tree classifications instead
of individual files, the disclosed systems and methods may more
accurately calculate file prevalence and identify the correct scope
of a security threat for suspicious files and families of related
files using static attributes of the files. Furthermore, by
performing security actions on suspicious files based on
relationships to known malicious files and a prevalence of the
related security threat, the disclosed systems and methods may
better protect a network of computing devices against malware.
Thus, the systems and methods described herein may improve the
robustness of file prevalence scores that are used to detect
potentially malicious files.
FIG. 7 is a block diagram of an example computing system 710
capable of implementing one or more of the embodiments described
and/or illustrated herein. For example, all or a portion of
computing system 710 may perform and/or be a means for performing,
either alone or in combination with other elements, one or more of
the steps described herein (such as one or more of the steps
illustrated in FIG. 3). All or a portion of computing system 710
may also perform and/or be a means for performing any other steps,
methods, or processes described and/or illustrated herein.
Computing system 710 broadly represents any single or
multi-processor computing device or system capable of executing
computer-readable instructions. Examples of computing system 710
include, without limitation, workstations, laptops, client-side
terminals, servers, distributed computing systems, handheld
devices, or any other computing system or device. In its most basic
configuration, computing system 710 may include at least one
processor 714 and a system memory 716.
Processor 714 generally represents any type or form of physical
processing unit (e.g., a hardware-implemented central processing
unit) capable of processing data or interpreting and executing
instructions. In certain embodiments, processor 714 may receive
instructions from a software application or module. These
instructions may cause processor 714 to perform the functions of
one or more of the example embodiments described and/or illustrated
herein.
System memory 716 generally represents any type or form of volatile
or non-volatile storage device or medium capable of storing data
and/or other computer-readable instructions. Examples of system
memory 716 include, without limitation, Random Access Memory (RAM),
Read Only Memory (ROM), flash memory, or any other suitable memory
device. Although not required, in certain embodiments computing
system 710 may include both a volatile memory unit (such as, for
example, system memory 716) and a non-volatile storage device (such
as, for example, primary storage device 732, as described in detail
below). In one example, one or more of modules 102 from FIG. 1 may
be loaded into system memory 716.
In some examples, system memory 716 may store and/or load an
operating system 724 for execution by processor 714. In one
example, operating system 724 may include and/or represent software
that manages computer hardware and software resources and/or
provides common services to computer programs and/or applications
on computing system 710. Examples of operating system 624 include,
without limitation, LINUX, JUNOS, MICROSOFT WINDOWS, WINDOWS
MOBILE, MAC OS, APPLE'S IOS, UNIX, GOOGLE CHROME OS, GOOGLE'S
ANDROID, SOLARIS, variations of one or more of the same, and/or any
other suitable operating system.
In certain embodiments, example computing system 710 may also
include one or more components or elements in addition to processor
714 and system memory 716. For example, as illustrated in FIG. 7,
computing system 710 may include a memory controller 718, an
Input/Output (I/O) controller 720, and a communication interface
722, each of which may be interconnected via a communication
infrastructure 712. Communication infrastructure 712 generally
represents any type or form of infrastructure capable of
facilitating communication between one or more components of a
computing device. Examples of communication infrastructure 712
include, without limitation, a communication bus (such as an
Industry Standard Architecture (ISA), Peripheral Component
Interconnect (PCI), PCI Express (PCIe), or similar bus) and a
network.
Memory controller 718 generally represents any type or form of
device capable of handling memory or data or controlling
communication between one or more components of computing system
710. For example, in certain embodiments memory controller 718 may
control communication between processor 714, system memory 716, and
I/O controller 720 via communication infrastructure 712.
I/O controller 720 generally represents any type or form of module
capable of coordinating and/or controlling the input and output
functions of a computing device. For example, in certain
embodiments I/O controller 720 may control or facilitate transfer
of data between one or more elements of computing system 710, such
as processor 714, system memory 716, communication interface 722,
display adapter 726, input interface 730, and storage interface
734.
As illustrated in FIG. 7, computing system 710 may also include at
least one display device 724 coupled to I/O controller 720 via a
display adapter 726. Display device 724 generally represents any
type or form of device capable of visually displaying information
forwarded by display adapter 726. Similarly, display adapter 726
generally represents any type or form of device configured to
forward graphics, text, and other data from communication
infrastructure 712 (or from a frame buffer, as known in the art)
for display on display device 724.
As illustrated in FIG. 7, example computing system 710 may also
include at least one input device 728 coupled to I/O controller 720
via an input interface 730. Input device 728 generally represents
any type or form of input device capable of providing input, either
computer or human generated, to example computing system 710.
Examples of input device 728 include, without limitation, a
keyboard, a pointing device, a speech recognition device,
variations or combinations of one or more of the same, and/or any
other input device.
Additionally or alternatively, example computing system 710 may
include additional I/O devices. For example, example computing
system 710 may include I/O device 736. In this example, I/O device
736 may include and/or represent a user interface that facilitates
human interaction with computing system 710. Examples of I/O device
736 include, without limitation, a computer mouse, a keyboard, a
monitor, a printer, a modem, a camera, a scanner, a microphone, a
touchscreen device, variations or combinations of one or more of
the same, and/or any other I/O device.
Communication interface 722 broadly represents any type or form of
communication device or adapter capable of facilitating
communication between example computing system 710 and one or more
additional devices. For example, in certain embodiments
communication interface 722 may facilitate communication between
computing system 710 and a private or public network including
additional computing systems. Examples of communication interface
722 include, without limitation, a wired network interface (such as
a network interface card), a wireless network interface (such as a
wireless network interface card), a modem, and any other suitable
interface. In at least one embodiment, communication interface 722
may provide a direct connection to a remote server via a direct
link to a network, such as the Internet. Communication interface
722 may also indirectly provide such a connection through, for
example, a local area network (such as an Ethernet network), a
personal area network, a telephone or cable network, a cellular
telephone connection, a satellite data connection, or any other
suitable connection.
In certain embodiments, communication interface 722 may also
represent a host adapter configured to facilitate communication
between computing system 710 and one or more additional network or
storage devices via an external bus or communications channel.
Examples of host adapters include, without limitation, Small
Computer System Interface (SCSI) host adapters, Universal Serial
Bus (USB) host adapters, Institute of Electrical and Electronics
Engineers (IEEE) 1394 host adapters, Advanced Technology Attachment
(ATA), Parallel ATA (PATA), Serial ATA (SATA), and External SATA
(eSATA) host adapters, Fibre Channel interface adapters, Ethernet
adapters, or the like. Communication interface 722 may also allow
computing system 710 to engage in distributed or remote computing.
For example, communication interface 722 may receive instructions
from a remote device or send instructions to a remote device for
execution.
In some examples, system memory 716 may store and/or load a network
communication program 738 for execution by processor 714. In one
example, network communication program 738 may include and/or
represent software that enables computing system 710 to establish a
network connection 742 with another computing system (not
illustrated in FIG. 7) and/or communicate with the other computing
system by way of communication interface 722. In this example,
network communication program 738 may direct the flow of outgoing
traffic that is sent to the other computing system via network
connection 742. Additionally or alternatively, network
communication program 738 may direct the processing of incoming
traffic that is received from the other computing system via
network connection 742 in connection with processor 714.
Although not illustrated in this way in FIG. 7, network
communication program 738 may alternatively be stored and/or loaded
in communication interface 722. For example, network communication
program 738 may include and/or represent at least a portion of
software and/or firmware that is executed by a processor and/or
Application-Specific Integrated Circuit (ASIC) incorporated in
communication interface 722.
As illustrated in FIG. 7, example computing system 710 may also
include a primary storage device 732 and a backup storage device
733 coupled to communication infrastructure 712 via a storage
interface 734. Storage devices 732 and 733 generally represent any
type or form of storage device or medium capable of storing data
and/or other computer-readable instructions. For example, storage
devices 732 and 733 may be a magnetic disk drive (e.g., a so-called
hard drive), a solid state drive, a floppy disk drive, a magnetic
tape drive, an optical disk drive, a flash drive, or the like.
Storage interface 734 generally represents any type or form of
interface or device for transferring data between storage devices
732 and 733 and other components of computing system 710. In one
example, database 120 from FIG. 1 may be stored and/or loaded in
primary storage device 732.
In certain embodiments, storage devices 732 and 733 may be
configured to read from and/or write to a removable storage unit
configured to store computer software, data, or other
computer-readable information. Examples of suitable removable
storage units include, without limitation, a floppy disk, a
magnetic tape, an optical disk, a flash memory device, or the like.
Storage devices 732 and 733 may also include other similar
structures or devices for allowing computer software, data, or
other computer-readable instructions to be loaded into computing
system 710. For example, storage devices 732 and 733 may be
configured to read and write software, data, or other
computer-readable information. Storage devices 732 and 733 may also
be a part of computing system 710 or may be a separate device
accessed through other interface systems.
Many other devices or subsystems may be connected to computing
system 710. Conversely, all of the components and devices
illustrated in FIG. 7 need not be present to practice the
embodiments described and/or illustrated herein. The devices and
subsystems referenced above may also be interconnected in different
ways from that shown in FIG. 7. Computing system 710 may also
employ any number of software, firmware, and/or hardware
configurations. For example, one or more of the example embodiments
disclosed herein may be encoded as a computer program (also
referred to as computer software, software applications,
computer-readable instructions, or computer control logic) on a
computer-readable medium. The term "computer-readable medium," as
used herein, generally refers to any form of device, carrier, or
medium capable of storing or carrying computer-readable
instructions. Examples of computer-readable media include, without
limitation, transmission-type media, such as carrier waves, and
non-transitory-type media, such as magnetic-storage media (e.g.,
hard disk drives, tape drives, and floppy disks), optical-storage
media (e.g., Compact Disks (CDs), Digital Video Disks (DVDs), and
BLU-RAY disks), electronic-storage media (e.g., solid-state drives
and flash media), and other distribution systems.
The computer-readable medium containing the computer program may be
loaded into computing system 710. All or a portion of the computer
program stored on the computer-readable medium may then be stored
in system memory 716 and/or various portions of storage devices 732
and 733. When executed by processor 714, a computer program loaded
into computing system 710 may cause processor 714 to perform and/or
be a means for performing the functions of one or more of the
example embodiments described and/or illustrated herein.
Additionally or alternatively, one or more of the example
embodiments described and/or illustrated herein may be implemented
in firmware and/or hardware. For example, computing system 710 may
be configured as an ASIC adapted to implement one or more of the
example embodiments disclosed herein.
FIG. 8 is a block diagram of an example network architecture 800 in
which client systems 810, 820, and 830 and servers 840 and 845 may
be coupled to a network 850. As detailed above, all or a portion of
network architecture 800 may perform and/or be a means for
performing, either alone or in combination with other elements, one
or more of the steps disclosed herein (such as one or more of the
steps illustrated in FIG. 3). All or a portion of network
architecture 800 may also be used to perform and/or be a means for
performing other steps and features set forth in the instant
disclosure.
Client systems 810, 820, and 830 generally represent any type or
form of computing device or system, such as example computing
system 710 in FIG. 7. Similarly, servers 840 and 845 generally
represent computing devices or systems, such as application servers
or database servers, configured to provide various database
services and/or run certain software applications. Network 850
generally represents any telecommunication or computer network
including, for example, an intranet, a WAN, a LAN, a PAN, or the
Internet. In one example, client systems 810, 820, and/or 830
and/or servers 840 and/or 845 may include all or a portion of
system 100 from FIG. 1.
As illustrated in FIG. 8, one or more storage devices 860(1)-(N)
may be directly attached to server 840. Similarly, one or more
storage devices 870(1)-(N) may be directly attached to server 845.
Storage devices 860(1)-(N) and storage devices 870(1)-(N) generally
represent any type or form of storage device or medium capable of
storing data and/or other computer-readable instructions. In
certain embodiments, storage devices 860(1)-(N) and storage devices
870(1)-(N) may represent Network-Attached Storage (NAS) devices
configured to communicate with servers 840 and 845 using various
protocols, such as Network File System (NFS), Server Message Block
(SMB), or Common Internet File System (CIFS).
Servers 840 and 845 may also be connected to a Storage Area Network
(SAN) fabric 880. SAN fabric 880 generally represents any type or
form of computer network or architecture capable of facilitating
communication between a plurality of storage devices. SAN fabric
880 may facilitate communication between servers 840 and 845 and a
plurality of storage devices 890(1)-(N) and/or an intelligent
storage array 895. SAN fabric 880 may also facilitate, via network
850 and servers 840 and 845, communication between client systems
810, 820, and 830 and storage devices 890(1)-(N) and/or intelligent
storage array 895 in such a manner that devices 890(1)-(N) and
array 895 appear as locally attached devices to client systems 810,
820, and 830. As with storage devices 860(1)-(N) and storage
devices 870(1)-(N), storage devices 890(1)-(N) and intelligent
storage array 895 generally represent any type or form of storage
device or medium capable of storing data and/or other
computer-readable instructions.
In certain embodiments, and with reference to example computing
system 710 of FIG. 7, a communication interface, such as
communication interface 722 in FIG. 7, may be used to provide
connectivity between each client system 810, 820, and 830 and
network 850. Client systems 810, 820, and 830 may be able to access
information on server 840 or 845 using, for example, a web browser
or other client software. Such software may allow client systems
810, 820, and 830 to access data hosted by server 840, server 845,
storage devices 860(1)-(N), storage devices 870(1)-(N), storage
devices 890(1)-(N), or intelligent storage array 895. Although FIG.
8 depicts the use of a network (such as the Internet) for
exchanging data, the embodiments described and/or illustrated
herein are not limited to the Internet or any particular
network-based environment.
In at least one embodiment, all or a portion of one or more of the
example embodiments disclosed herein may be encoded as a computer
program and loaded onto and executed by server 840, server 845,
storage devices 860(1)-(N), storage devices 870(1)-(N), storage
devices 890(1)-(N), intelligent storage array 895, or any
combination thereof. All or a portion of one or more of the example
embodiments disclosed herein may also be encoded as a computer
program, stored in server 840, run by server 845, and distributed
to client systems 810, 820, and 830 over network 850.
As detailed above, computing system 710 and/or one or more
components of network architecture 800 may perform and/or be a
means for performing, either alone or in combination with other
elements, one or more steps of an example method for identifying
malicious computer files.
While the foregoing disclosure sets forth various embodiments using
specific block diagrams, flowcharts, and examples, each block
diagram component, flowchart step, operation, and/or component
described and/or illustrated herein may be implemented,
individually and/or collectively, using a wide range of hardware,
software, or firmware (or any combination thereof) configurations.
In addition, any disclosure of components contained within other
components should be considered examples in nature since many other
architectures can be implemented to achieve the same
functionality.
In some examples, all or a portion of example system 100 in FIG. 1
may represent portions of a cloud-computing or network-based
environment. Cloud-computing environments may provide various
services and applications via the Internet. These cloud-based
services (e.g., software as a service, platform as a service,
infrastructure as a service, etc.) may be accessible through a web
browser or other remote interface. Various functions described
herein may be provided through a remote desktop environment or any
other cloud-based computing environment.
In various embodiments, all or a portion of example system 100 in
FIG. 1 may facilitate multi-tenancy within a cloud-based computing
environment. In other words, the software modules described herein
may configure a computing system (e.g., a server) to facilitate
multi-tenancy for one or more of the functions described herein.
For example, one or more of the software modules described herein
may program a server to enable two or more clients (e.g.,
customers) to share an application that is running on the server. A
server programmed in this manner may share an application,
operating system, processing system, and/or storage system among
multiple customers (i.e., tenants). One or more of the modules
described herein may also partition data and/or configuration
information of a multi-tenant application for each customer such
that one customer cannot access data and/or configuration
information of another customer.
According to various embodiments, all or a portion of example
system 100 in FIG. 1 may be implemented within a virtual
environment. For example, the modules and/or data described herein
may reside and/or execute within a virtual machine. As used herein,
the term "virtual machine" generally refers to any operating system
environment that is abstracted from computing hardware by a virtual
machine manager (e.g., a hypervisor). Additionally or
alternatively, the modules and/or data described herein may reside
and/or execute within a virtualization layer. As used herein, the
term "virtualization layer" generally refers to any data layer
and/or application layer that overlays and/or is abstracted from an
operating system environment. A virtualization layer may be managed
by a software virtualization solution (e.g., a file system filter)
that presents the virtualization layer as though it were part of an
underlying base operating system. For example, a software
virtualization solution may redirect calls that are initially
directed to locations within a base file system and/or registry to
locations within a virtualization layer.
In some examples, all or a portion of example system 100 in FIG. 1
may represent portions of a mobile computing environment. Mobile
computing environments may be implemented by a wide range of mobile
computing devices, including mobile phones, tablet computers,
e-book readers, personal digital assistants, wearable computing
devices (e.g., computing devices with a head-mounted display,
smartwatches, etc.), and the like. In some examples, mobile
computing environments may have one or more distinct features,
including, for example, reliance on battery power, presenting only
one foreground application at any given time, remote management
features, touchscreen features, location and movement data (e.g.,
provided by Global Positioning Systems, gyroscopes, accelerometers,
etc.), restricted platforms that restrict modifications to
system-level configurations and/or that limit the ability of
third-party software to inspect the behavior of other applications,
controls to restrict the installation of applications (e.g., to
only originate from approved application stores), etc. Various
functions described herein may be provided for a mobile computing
environment and/or may interact with a mobile computing
environment.
In addition, all or a portion of example system 100 in FIG. 1 may
represent portions of, interact with, consume data produced by,
and/or produce data consumed by one or more systems for information
management. As used herein, the term "information management" may
refer to the protection, organization, and/or storage of data.
Examples of systems for information management may include, without
limitation, storage systems, backup systems, archival systems,
replication systems, high availability systems, data search
systems, virtualization systems, and the like.
In some embodiments, all or a portion of example system 100 in FIG.
1 may represent portions of, produce data protected by, and/or
communicate with one or more systems for information security. As
used herein, the term "information security" may refer to the
control of access to protected data. Examples of systems for
information security may include, without limitation, systems
providing managed security services, data loss prevention systems,
identity authentication systems, access control systems, encryption
systems, policy compliance systems, intrusion detection and
prevention systems, electronic discovery systems, and the like.
According to some examples, all or a portion of example system 100
in FIG. 1 may represent portions of, communicate with, and/or
receive protection from one or more systems for endpoint security.
As used herein, the term "endpoint security" may refer to the
protection of endpoint systems from unauthorized and/or
illegitimate use, access, and/or control. Examples of systems for
endpoint protection may include, without limitation, anti-malware
systems, user authentication systems, encryption systems, privacy
systems, spam-filtering services, and the like.
The process parameters and sequence of steps described and/or
illustrated herein are given by way of example only and can be
varied as desired. For example, while the steps illustrated and/or
described herein may be shown or discussed in a particular order,
these steps do not necessarily need to be performed in the order
illustrated or discussed. The various example methods described
and/or illustrated herein may also omit one or more of the steps
described or illustrated herein or include additional steps in
addition to those disclosed.
While various embodiments have been described and/or illustrated
herein in the context of fully functional computing systems, one or
more of these example embodiments may be distributed as a program
product in a variety of forms, regardless of the particular type of
computer-readable media used to actually carry out the
distribution. The embodiments disclosed herein may also be
implemented using software modules that perform certain tasks.
These software modules may include script, batch, or other
executable files that may be stored on a computer-readable storage
medium or in a computing system. In some embodiments, these
software modules may configure a computing system to perform one or
more of the example embodiments disclosed herein.
In addition, one or more of the modules described herein may
transform data, physical devices, and/or representations of
physical devices from one form to another. For example, one or more
of the modules recited herein may receive file attributes to be
transformed, transform the file attributes, output a result of the
transformation to a storage or output device, use the result of the
transformation to create a set of decision trees, and store the
result of the transformation in a server or database. Additionally
or alternatively, one or more of the modules recited herein may
transform a processor, volatile memory, non-volatile memory, and/or
any other portion of a physical computing device from one form to
another by executing on the computing device, storing data on the
computing device, and/or otherwise interacting with the computing
device.
The preceding description has been provided to enable others
skilled in the art to best utilize various aspects of the example
embodiments disclosed herein. This example description is not
intended to be exhaustive or to be limited to any precise form
disclosed. Many modifications and variations are possible without
departing from the spirit and scope of the instant disclosure. The
embodiments disclosed herein should be considered in all respects
illustrative and not restrictive. Reference should be made to the
appended claims and their equivalents in determining the scope of
the instant disclosure.
Unless otherwise noted, the terms "connected to" and "coupled to"
(and their derivatives), as used in the specification and claims,
are to be construed as permitting both direct and indirect (i.e.,
via other elements or components) connection. In addition, the
terms "a" or "an," as used in the specification and claims, are to
be construed as meaning "at least one of." Finally, for ease of
use, the terms "including" and "having" (and their derivatives), as
used in the specification and claims, are interchangeable with and
have the same meaning as the word "comprising."
* * * * *